Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems
This work addresses the challenge of generating language in new scenarios with limited data for task-oriented dialogue systems, representing an incremental improvement over existing meta-learning methods.
The paper tackles the problem of natural language generation in task-oriented dialogue systems under low-resource conditions, proposing Meta-NLG, a meta-learning approach that significantly outperforms other methods in experiments on the MultiWoz dataset with fast adaptation.
Natural language generation (NLG) is an essential component of task-oriented dialogue systems. Despite the recent success of neural approaches for NLG, they are typically developed for particular domains with rich annotated training examples. In this paper, we study NLG in a low-resource setting to generate sentences in new scenarios with handful training examples. We formulate the problem from a meta-learning perspective, and propose a generalized optimization-based approach (Meta-NLG) based on the well-recognized model-agnostic meta-learning (MAML) algorithm. Meta-NLG defines a set of meta tasks, and directly incorporates the objective of adapting to new low-resource NLG tasks into the meta-learning optimization process. Extensive experiments are conducted on a large multi-domain dataset (MultiWoz) with diverse linguistic variations. We show that Meta-NLG significantly outperforms other training procedures in various low-resource configurations. We analyze the results, and demonstrate that Meta-NLG adapts extremely fast and well to low-resource situations.